Method and system for predicting human cognitive performance

ABSTRACT

An apparatus and a method for predicting cognitive performance of an individual based on factors including preferably sleep history and the time of day. The method facilitates the creation of predicted cognitive performance curves that allow an individual to set his/her sleep times to produce higher levels of cognitive performance. The method also facilitates the reconstruction of past cognitive performance levels based on sleep history.

This application is a continuation of U.S. application Ser. No. 10/377,602, filed on Mar. 4, 2003, now U.S. Pat. No. 6,740,032, which is a continuation of U.S. application Ser. No. 09/844,434, filed on Apr. 30, 2001, now U.S. Pat. No. 6,530,884, which is a continuation-in-part of PCT Application No. PCT/US99/20092, filed Sep. 3, 1999 (which designates the United States and was published on May 11, 2000), which claims priority from U.S. provisional Application Ser. No. 60/106,419, filed Oct. 30, 1998, and U.S. provisional Application Ser. No. 60/122,407, filed Mar. 2, 1999; and U.S. application Ser. No. 09/844,434 claims the benefit of U.S. provisional Application Ser. No. 60/273,540, filed Mar. 7, 2001. These patent applications are hereby incorporated by reference.

I. FIELD OF THE INVENTION

This invention relates to a method for predicting cognitive performance of an individual preferably based on that individual's prior sleep/wake history, the time of day, and tasks (or activities) being performed by the individual.

II. BACKGROUND OF THE INVENTION

Maintenance of productivity in any workplace setting depends upon effective cognitive performance at all levels from command/control or management down to the individual soldier or worker. Effective cognitive performance in turn depends upon complex mental operations. Many factors have been shown to affect cognitive performance (e.g., drugs or age). However, of the numerous factors causing day to day variations in cognitive performance, two have been shown to have the greatest impact. These two factors are an individual's prior sleep/wake history and the time of day.

Adequate sleep sustains cognitive performance. With less than adequate sleep, cognitive performance degrades over time. An article by Thorne et al. entitled “Plumbing Human Performance Limits During 72 hours of High Task Load” in Proceedings of the 24^(th) DRG Seminar on the Human as a Limiting Element in Military Systems, Defense and Civil Institute of Environmental Medicine, pp. 17-40 (1983), an article by Newhouse et al. entitled “The Effects of d-Amphetamine on Arousal, Cognition, and Mood After Prolonged Total Sleep Deprivation” published in Neuropsychopharmacology, vol. 2, pp. 153-164 (1989), and another article by Newhouse et al. entitled “Stimulant Drug Effects on Performance and Behavior After Prolonged Sleep Deprivation: A Comparison of Amphetamine, Nicotine, and Deprenyl” published in Military Psychology, vol. 4, pp. 207-233 (1992) all describe studies of normal volunteers in which it is revealed that robust, cumulative decrements in cognitive performance occur during continuous total sleep deprivation as measured by computer-based testing and complex operational simulation. In the Dinges et al. article entitled “Cumulative Sleepiness, Mood Disturbance, and Psychomotor Vigilance Performance Decrements During a Week of Sleep Restricted to 4-5 Hours Per Night” published in Sleep, vol. 20, pp. 267-277 (1997), it is revealed that on fixed, restricted daily sleep amounts, cumulative reduced sleep also leads to a cognitive performance decline. Thus, in operational settings, both civilian and military, sleep deprivation reduces productivity (output of useful work per unit of time) on cognitive tasks.

Thus, using computer-based cognitive performance tests, it has been shown that total sleep deprivation degrades human cognitive performance by approximately 25% for each successive period of 24 hours awake. However, it also has been shown that even small amounts of sleep reduce the rate of sleep loss-induced cognitive performance degradation. Belenky et al. in their article entitled “Sustaining Performance During Continuous Operations: The U.S. Army's Sleep Management System,” published in 20^(th) Army Science Conference Proceedings, vol. 2, pp. 657-661 (1996) disclose that a single 30-minute nap every 24 hours reduces the rate of cognitive performance degradation to 17% per day over 85 hours of sleep deprivation. This suggests that recuperation of cognitive performance during sleep accrues most rapidly early in the sleep period. No other factor besides the amount of sleep contributes so substantially and consistently to the normal, daily variations in cognitive performance.

In addition to sleep/wake history, an individual's cognitive performance at a given point in time is determined by the time of day. In the early 1950s, Franz Halberg and associates observed a 24-hour periodicity in a host of human physiologic (including body temperature and activity), hematologic, and hormonal functions, and coined the term ‘circadian’ (Latin for ‘about a day’) to describe this cyclic rhythm. Halberg showed that most noise in experimental data came from comparisons of data sampled at different times of day.

When humans follow a nocturnal sleep/diurnal wake schedule (for example, an 8-hour sleep/16-hour wake cycle, with nightly sleep commencing at approximately midnight), body temperature reaches a minimum (trough) usually between 2:00 AM and 6:00 AM. Body temperature then begins rising to a maximum (peak) usually between 8:00 PM and 10:00 PM. Likewise, systematic studies of daily human cognitive performance rhythms show that speed of responding slowly improves across the day to reach a maximum in the evening (usually between 8:00 PM and 10:00 PM) then dropping more rapidly to a minimum occurring in the early morning hours (usually between 2:00 AM and 6:00 AM). Similar but somewhat less consistent rhythms have been shown from testing based on various cognitive performance tasks. Thus, superimposed on the effect of total sleep deprivation on cognitive performance noted above was an approximately ±10% variation in cognitive performance over each 24-hour period.

Various measures have been shown to correlate, to some extent, with cognitive performance. These include objective and subjective measures of sleepiness (or its converse, alertness). Some individuals familiar with the art use “sleepiness” to indicate the opposite of “alertness” (as is the case in the present document). “Drowsiness” often is used interchangeably with “sleepiness” although some familiar with the art would argue that “sleepiness” pertains specifically to the physiological need for sleep whereas “drowsiness” refers more to the propensity or ability to fall asleep (independent of physiological sleep need) or the subjective feeling of lack of alertness. The term “fatigue” has been used as a synonym for “sleepiness” by the lay population, but those familiar with the art do not consider “fatigue” to be interchangeable with “sleepiness”—rather, “fatigue” is a broad term that encompasses more than just the effects of sleep loss per se on performance. Likewise, “cognitive performance” has been defined as performance on a wide variety of tasks, the most commonly used being vigilance tasks (tasks requiring sustained attention). From vigilance and other tasks, some researchers use accuracy as their measure of cognitive performance, while others use reaction time (or its inverse, speed). Still others use a measure that is calculated as speed multiplied by accuracy, that is the amount of useful work performed per unit of time (also known as throughput). Those familiar with the art generally agree that vigilance tasks are appropriate measures of cognitive performance under conditions of sleep deprivation, and that either reaction time (speed) or some measure that takes reaction time into account (e.g., throughput) is a valid and reliable way of measuring cognitive performance.

The Multiple Sleep Latency Test (MSLT) is a widely accepted objective measure of sleepiness/alertness. In the MSLT, individuals try to fall asleep while lying in a darkened, quiet bedroom. Various physiological measures used to determine sleep or wakefulness are recorded (eye movements, brain activity, muscle tone), and time taken to reach the first 30 seconds of stage 1 (light) sleep is determined. Shorter latencies to stage 1 are considered to indicate greater sleepiness (lower alertness). Sleep latencies under 5 minutes are considered to be pathological (i.e., indicative of a sleep disorder or sleep deprivation). During both total and partial sleep deprivation, latency to sleep on the MSLT (alertness) and performance decline (i.e., sleepiness as measured by MSLT increases). However, although there is a correlation between MSLT-determined sleepiness/alertness and cognitive performance (greater sleepiness as indexed by MSLT corresponding to poorer cognitive performance), this correlation has never been shown to be perfect and for the most part is not strong. As a result, the MSLT is a poor (i.e., unreliable) predictor of cognitive performance.

Subjective measures of sleepiness/alertness also have been shown to correlate (albeit weakly) with cognitive performance. Hoddes et al. in their article entitled “Quantification of Sleepiness: A New Approach” published in Psychophysiology, vol. 10, pp. 431-436 (1973) describe the Stanford Sleepiness Scale (SSS), a subjective questionnaire used widely to measure sleepiness/alertness. In the SSS, individuals rate their current level of sleepiness/alertness on a scale from 1 to 7, with 1 corresponding to the statement, “feeling active and vital; alert; wide awake” and 7 corresponding to the statement “almost in reverie; sleep onset soon; losing struggle to remain awake.” Higher SSS scores indicate greater sleepiness. As with the MSLT, during both total and partial sleep deprivation, scores on the SSS increase. However, as with MSLT, the correspondence between SSS-determined sleepiness/alertness and cognitive performance decrements is weak and inconsistent. As a result, the SSS also is a poor predictor of cognitive performance. Some other examples of subjective measures of sleepiness/alertness include the Epworth Sleepiness Scale described by Johns in his article entitled “Daytime Sleepiness, Snoring, and Obstructive Sleep Apnea” published in Chest, vol. 103, pp. 30-36 (1993) and the Karolinska Sleepiness scale described by Akerstedt and Gillberg in their article entitled “Subjective and Objective Sleepiness in the Active Individual” published in International Journal of Neuroscience, vol. 52, pp. 29-37 (1990). The correspondence between these subjective measures and cognitive performance also is weak and inconsistent.

In addition, factors modifying cognitive performance may not correspondingly affect objective or subjective measures of sleepiness/alertness, and vice versa. For example, the Penetar et al. article entitled “Amphetamine Effects on Recovery Sleep Following Total Sleep Deprivation” published in Human Psychopharmacology, vol. 6, pp. 319-323 (1991) discloses that during sleep deprivation, the stimulant drug d-amphetamine improved cognitive performance but not sleepiness/alertness (as measured by the MSLT). In a similar study, caffeine given as a sleep deprivation countermeasure maintained elevated cognitive performance for over 12 hours while the effects on subjective sleepiness, vigor and fatigue transiently improved but then decayed. Thorne et al. in their article entitled “Plumbing Human Performance Limits During 72 hours of High Task Load” in Proceedings of the 24^(th) DRG Seminar on the Human as a Limiting Element in Military Systems, Defense and Civil Institute of Environmental Medicine, pp. 17-40 (1983) describe how cognitive performance continues to decline over 72 hours of sleep deprivation whereas subjective sleepiness/alertness declined over the first 24 hours but subsequently leveled off. The findings that cognitive performance and measures of sleepiness/alertness are not always affected in the same way indicate that they are not interchangeable. That is, measures of sleepiness/alertness cannot be used to predict cognitive performance, and vice versa.

Methods and apparatuses related to alertness detection fall into five basic categories: a method/apparatus for unobtrusively monitoring current alertness level; a method/apparatus for unobtrusively monitoring current alertness level and providing a warning/alarm to the user of decreased alertness and/or to increase user's alertness level; a method/apparatus for monitoring current alertness level based on the user's responses to some secondary task possibly with an alarm device to warn the user of decreased alertness and/or to increase user's alertness level; methods to increase alertness; and a method/apparatus for predicting past, current, or future alertness.

These methods and apparatuses that unobtrusively monitor the current alertness level are based on an “embedded measures” approach. That is, such methods infer alertness/drowsiness from the current level of some factor (e.g., eye position or closure) assumed to correlate with alertness/drowsiness. Issued patents of this type include U.S. Pat. No. 5,689,241 to J. Clarke, Sr., et al. disclosing an apparatus to detect eye closure and ambient temperature around the nose and mouth; U.S. Pat. No. 5,682,144 to K. Mannik disclosing an apparatus to detect eye closure; and U.S. Pat. No. 5,570,698 to C. Liang et al. disclosing an apparatus to monitor eye localization and motion to detect sleepiness. An obvious disadvantage of these types of methods and apparatuses is that the measures are likely detecting sleep onset itself rather than small decreases in alertness.

In some patents, methods for embedded monitoring of alertness/drowsiness are combined with additional methods for signaling the user of decreased alertness and/or increasing alertness. Issued patents of this type include U.S. Pat. No. 5,691,693 to P. Kithil describing a device that senses a vehicle operator's head position and motion to compare current data to profiles of “normal” head motion and “impaired” head motion. Warning devices are activated when head motion deviates from the “normal” in some predetermined way. U.S. Pat. No. 5,585,785 to R. Gwin et al. describes an apparatus and a method for measuring total handgrip pressure on a steering wheel such that an alarm is sounded when the grip pressure falls below a predetermined “lower limit” indicating drowsiness. U.S. Pat. No. 5,568,127 to H. Bang describes a device for detecting drowsiness as indicated by the user's chin contacting an alarm device, which then produces a tactile and auditory warning. U.S. Pat. No. 5,566,067 to J. Hobson et al. describes a method and an apparatus to detect eyelid movements. A change in detected eyelid movements from a predetermined threshold causes an output signal/alarm (preferably auditory). As with the first category of methods and apparatuses, a disadvantage here is that the measures are likely detecting sleep onset itself rather than small decreases in alertness.

Other alertness/drowsiness monitoring devices have been developed based on a “primary/secondary task” approach. For example, U.S. Pat. No. 5,595,488 to E. Gozlan et al. describes an apparatus and a method for presenting auditory, visual, or tactile stimuli to an individual to which the individual must respond (secondary task) while performing the primary task of interest (e.g., driving). Responses on the secondary task are compared to baseline “alert” levels for responding. U.S. Pat. No. 5,259,390 to A. MacLean describes a device in which the user responds to a relatively innocuous vibrating stimulus. The speed to respond to the stimulus is used as a measure of the alertness level. A disadvantage here is that the apparatus requires responses to a secondary task to infer alertness, thereby altering and possibly interfering with the primary task.

Other methods exist solely for increasing alertness, depending upon the user to self-evaluate alertness level and activate the device when the user feels drowsy. An example of the latter is U.S. Pat. No. 5,647,633 and related patents to M. Fukuoka in which a method/apparatus is described for causing the user's seat to vibrate when the user detects drowsiness. Obvious disadvantages of such devices are that the user must be able to accurately self-assess his/her current level of alertness, and that the user must be able to correctly act upon this assessment.

Methods also exist to predict alertness level based on user inputs known empirically to modify alertness. U.S. Pat. No. 5,433,223 to M. Moore-Ede et al. describes a method for predicting the likely alertness level of an individual at a specific point in time (past, current or future) based upon a mathematical computation of a variety of factors (referred to as “real-world” factors) that bear some relationship to alterations in alertness. The individual's Baseline Alertness Curve (BAC) is first determined based on five inputs and represents the optimal alertness curve displayed in a stable environment. Next, the BAC is modified by alertness modifying stimuli to arrive at a Modified Baseline Alertness Curve. Thus, the method is a means for predicting an individual's alertness level, not cognitive performance.

Another method has been designed to predict “work-related fatigue” as a function of number of hours on duty. Fletcher and Dawson describe their method in an article entitled “A Predictive Model of Work-Related Fatigue Based on Hours of Work” published in Journal of Occupational Health and Safety, vol. 13, 471-485 (1997). In this model a simplifying assumption is made—it is assumed that length of on-duty time correlates positively with time awake. To implement the method, the user inputs a real or hypothetical on-duty/off-duty (work/rest) schedule. Output from the model is a score that indicates “work-related fatigue.” Although this “work-related fatigue” score has been shown to correlate with some performance measures, it is not a direct measure of cognitive performance per se. It can be appreciated that the fatigue score will be less accurate under circumstances when the presumed relationship between on-duty time and time awake breaks down—for example when a person works a short shift but then spends time working on projects at home rather than sleeping or when a person works long shifts but conscientiously sleeps all the available time at home. Also, this method is obtrusive in that the user must input on-duty/off-duty information rather than such information being automatically extracted from an unobtrusive recording device. In addition, the model is limited to predictions of “fatigue” based on work hours. Overall, this model is limited to work-related situations in which shift length consistently correlates (inversely) with sleep length.

Given the importance of the amount of sleep and the time of day for determining cognitive performance (and hence estimating productivity or effectiveness), and given the ever-increasing requirements of most occupations on cognitive performance, it is desirable to design a reliable and accurate method of predicting cognitive performance. It can be appreciated that increasing the number of relevant inputs increases cognitive performance prediction accuracy. However, the relative benefits gained from such inputs must be weighed against the additional burdens/costs associated with their collection and input. For example, although certain fragrances have been shown to have alertness-enhancing properties, these effects are inconsistent and negligible compared to the robust effects of the individual's sleep/wake history and the time of day. More important, the effect of fragrances on cognitive performance is unknown. Requiring an individual to keep a log of exposure to fragrances would be time consuming to the individual and only result in negligible gains in cognitive performance prediction accuracy. In addition, while the effects of the sleep/wake history and the time of day on cognitive performance are well known, the effects of other putative alertness-altering factors (e.g., job stress), how to measure them (their operational definition), and their direction of action (cognitive performance enhancing or degrading) are virtually unknown.

An important and critical distinction between the present invention and the prior art is that the present invention is a model to predict performance on tasks with a cognitive component. In contrast, previous models involving sleep and/or circadian rhythms (approximately 24-hour) focused on the prediction of “alertness” or “sleepiness.” The latter are concepts that specifically relate to the propensity to initiate sleep, not the ability to perform a cognitive task.

Although sleepiness (or its converse, alertness) could be viewed as an intervening variable that can mediate cognitive performance, the scientific literature clearly shows that cognitive performance and alertness are conceptually distinct, as reviewed by Johns in the article entitled “Rethinking the Assessment of Sleepiness” published in Sleep Medicine Reviews, vol. 2, pp. 3-15 (1998) and as reviewed by Mitler et al. in the article entitled “Methods of Testing for Sleepiness” published in Behavioral Medicine, vol. 21, pp. 171-183 (1996). Thomas et al. in the article entitled “Regional Cerebral Metabolic Effects of Prolonged Sleep Deprivation” published in NeuroImage, vol. 7, p. S130 (1998) reveal that 1-3 days of sleep loss result in reductions in global brain activation of approximately 6%, as measured by regional cerebral glucose uptake. However, those regions (heteromodal association cortices) that mediate the highest order cognitive functions (including but not limited to attention, vigilance, situational awareness, planning, judgment, and decision making) are selectively deactivated by sleep loss to a much greater extent—up to 50%—after three days of sleep loss. Thus, decreases in neurobiological functioning during sleep restriction/deprivation are directly reflected in cognitive performance degradation. These findings are consistent with studies demonstrating that tasks requiring higher-order cognitive functions, especially those tasks requiring attention, planning, etc. (abilities mediated by heteromodal association areas) are especially sensitive to sleep loss. On the other hand, brain regions such as primary sensory regions, are deactivated to a lesser degree. Concomitantly, performance (e.g., vision, hearing, strength and endurance tasks) that is dependent on these regions is virtually unaffected by sleep loss.

Consequently, devices or inventions that predict “alertness” per se (e.g., Moore-Ede et al.) putatively quantify the brain's underlying propensity to initiate sleep at any given point in time. That is, devices or inventions that predict “alertness” (or its converse “sleepiness”) predict the extent to which sleep onset is likely. The present invention differs from such approaches in that the nature of the task is accounted for—i.e., it is not the propensity to initiate sleep that is predicted. Rather, the present invention predicts the extent to which performance of a particular task will be impaired by virtue of its reliance upon brain areas most affected by sleep deprivation (heteromodal association areas of the brain). The most desirable method will produce a highly reliable and accurate cognitive performance estimate based on the sleep/wake history of an individual, the time of day, and the amount of time on a particular task.

III. SUMMARY OF THE INVENTION

A method for providing a cognitive performance level in accordance with the invention includes receiving a data series representing at least one wake state and at least one sleep state, selecting a function based on the data series, determining a cognitive performance capacity using the selected function, modulating the cognitive performance capacity with a time of day value, and providing the modulated value.

An apparatus for providing a cognitive performance level in accordance with the invention includes means for receiving a data series having at least one wake state and at least one sleep state, means for selecting a function based on the data series, means for determining a cognitive performance capacity using the selected function, means for storing a series of time of day values, means for modulating the cognitive performance capacity with a corresponding time of day value, and means for providing the modulated value.

A method for determining a cognitive performance level in accordance with the invention includes inputting a data series having wake states and sleep states of an individual, selecting a function based on the wake states and sleep states in the data series, calculating a cognitive performance capacity based on the selected function, modulating the cognitive performance capacity with a time of day value, and outputting the modulated value as the predicted cognitive performance.

A method for determining a predicted cognitive performance in accordance with the invention includes inputting a data series having wake states and sleep states of an individual, selecting a function based on the wake states and sleep states in the data series, calculating a cognitive performance capacity based on the selected function, approximating a first curve of calculated cognitive performance capacities, modulating the first curve with a second curve representing time of day rhythms, and outputting the modulated first curve representing the predicted cognitive performance.

A feature of the present invention is that it provides a numerical representation of predicted cognitive performance with an immediate ergonomic and economic advantage, i.e., an indication of productivity or effectiveness of an individual. Another feature of the present invention is that it does not require or use measurements/computations that are indirect, intermediate, inferential or hypothetical concomitants of cognitive performance. Examples of the latter are alertness, sleepiness, time to sleep onset, body temperature and/or other physiological measures that vary with time. A further feature of the invention is that it accounts for transient or adventitious variations in cognitive performance from any source as a result of how that source affects the sleep/wake history (e.g., age) and/or physiological time of day (e.g., shift work). In effect, such sources are not treated as having effects on cognitive performance independent of the sleep/wake history and/or the time of day, and as such do not require separate measurement, tabulation, and input into the method.

One objective of this invention is to provide an accurate method for predicting cognitive performance of an individual.

A further objective is to provide a method that facilitates prediction of the effects of possible future sleep/wake histories on cognitive performance (forward prediction).

Another objective is to provide a method that facilitates retrospective analysis of likely prior cognitive performance based on, for example, the individual's sleep/wake history, the time of day, and the activities done by the individual.

Another objective is to provide a method for coordination and optimization of available sleep/wake time in order to obtain net optimal predicted cognitive performance for an individual and/or a group of individuals.

It can be appreciated that an implicit advantage and novelty of the method is its parsimony. The method uses those factors possessing maximal predictive value (as demonstrated empirically) as continuously updated inputs. Thus, the model will be simple to implement. Other models predicting “alertness” require the user to track multiple input variables (e.g., caffeine, alcohol ingestion, light/dark exposure, diurnal type), rather than presenting these inputs as optional “attachments” to a standard, simplified model based on those factors accounting for maximum cognitive performance change. For example, in accordance with a segment of the present method, the effects of age on cognitive performance are accounted for implicitly via the empirically derived effects of age on sleep. That is, sleep quality degrades with age. The inherent degradation in sleep quality with aging would implicitly result in a prediction of degraded cognitive performance (since in the present method degraded sleep results in a prediction of degraded cognitive performance), even if an individual's age were unknown. Therefore, age need not constitute a separate (independent) input variable to a cognitive performance prediction model.

The invention also provides other significant advantages. For example, an advantage of this invention is the elimination of a need for empirical evaluation.

Another advantage of this invention is obtaining an accurate prediction of cognitive performance of an individual. The advantage may be achieved by a method incorporating, for example, at least two of the following factors that have been empirically demonstrated to exert a significant effect on cognitive performance, namely, (1) the individual's sleep/wake history, (2) the time of day (“day” herein referring to a 24-hour period including both nighttime and daylight hours), and (3) the individual's time on a particular task.

Another advantage achieved by this invention is an accurate prediction of current cognitive performance.

Another advantage achieved by this invention is that it is capable of providing a real time prediction of cognitive performance.

Yet another advantage achieved by this invention is a prediction of future expected cognitive performance throughout the day based on hypothetical future sleep/wake periods.

An additional advantage achieved by this invention is a retrospective analysis of cognitive performance at given times.

A further advantage of the invention is that a particular cognitive performance prediction is not based on normative data (i.e., does not require a “look-up table” for output), but rather is calculated directly based on, for example as discussed in connection with one embodiment, each individual's sleep/wake information, the time of day, and the time on a task.

A further advantage of the invention is that it can be used to optimize the individual's future sleep/wake schedule based on a fixed mission/work schedule. Previous methods and apparatuses are directed toward modifying the work schedule and/or mission to “fit the individual.” In most situations, however, work schedules and/or missions are fixed. Thus, modifying the work schedule or mission to suit the individual is impractical or impossible. A more reasonable approach incorporated in the present method is to allow the individual to adjust his/her sleep/wake periods to meet work/mission demands. Thus, the current method presents a more practical alternative by providing a means to regulate work hours to a directly applicable metric (cognitive performance) instead of regulating work hours by time off duty or by using indirect measures of cognitive performance such as alertness.

A feature of this invention is the provision of a graphical representation that translates an individual's sleep/wake history and the time of day into an immediately useful, self-evident index. A prediction of cognitive performance, unlike a prediction of “alertness” or “sleepiness,” requires no further interpretation.

The method for predicting human cognitive performance in accordance with the invention accomplishes the above objectives and achieves the above advantages. The method and resulting apparatus are easily adapted to a Wide variety of situations and types of inputs.

In accordance with an aspect of the invention, an individual sleep/wake history is inputted into a processing device. The processing device classifies the individual pieces of sleep/wake history data as either sleep or wake. Based on the classification of data, the processing device selects and calculates a cognitive performance capacity corresponding to the present state of the individual, the cognitive performance capacity may be modified by a time of day value to adjust the cognitive performance capacity to a predicted cognitive performance. The predicted cognitive performance represents the ability of the individual to perform cognitive tasks. The predicted cognitive performance may be displayed for a real-time indication or as part of a curve, printed out with the information that could have been displayed, and/or stored for later retrieval and/or use. The calculation of the cognitive performance capacity is made based on functions that model the effect of the interrelationship of sleep and being awake on cognitive performance. The time of day function models the effect of an individual's circadian rhythms on cognitive performance.

In accordance with the underlying method of the invention, the method can be accomplished with a wide variety of apparatus. Examples of the possible apparatus embodiments include electronic hardware as either dedicated equipment or equipment internal to a computer, software embodied in computer readable material for use by computers, software resident in memory or a programmed chip for use in computers or dedicated equipment, or some combination of both hardware and software. The dedicated equipment may be part of a larger device that would complement the dedicated equipment's purpose.

Given the following enabling description of the drawings, the invention should become evident to a person of ordinary skill in the art.

IV. DESCRIPTION OF THE DRAWINGS

FIG. 1( a) is a conceptual diagram representation of the invention including the fine-tuning alternative embodiment.

FIG. 1( b) graphically shows the combination of output from the functions represented by FIG. 3( a) with time of day modulation to derive predicted cognitive performance.

FIG. 2 is a block diagram representation of the wake, sleep, delay, and sleep inertia functions for calculating predicted cognitive performance capacity.

FIG. 3( a) graphically illustrates the effect of being awake and asleep on cognitive performance capacity over a 24-hour period.

FIG. 3( b) is an enlarged view of circled portion 3(b) of FIG. 3( a), and graphically shows the delay function with respect to cognitive performance capacity.

FIG. 3( c) is an enlarged view of circled portion 3(c) of FIG. 3( a), and graphically shows the sleep inertia function with respect to cognitive performance capacity.

FIGS. 4( a)-(b) depict a detailed flowchart showing the steps of the method of the invention.

FIG. 5 illustrates time on task effects across a 10-minute Psychomotor Vigilance Task (PVT) sessions at two hour increments during 40 hours of total sleep deprivation.

FIG. 6 depicts a functional representation of an alternative embodiment.

FIG. 7( a) illustrates a block diagram of structural components for the preferred embodiment.

FIG. 7( b) illustrates a block diagram of an alternative set of structural components.

FIGS. 8( a)-(b) depict a detailed flowchart showing the steps of an alternative embodiment.

V. DETAILED DESCRIPTION OF THE INVENTION

The present invention now is described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. Like numbers refer to like elements throughout.

As will be appreciated by one of skill in the art, the present invention may be embodied as a method, data processing system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product on a computer-usable storage medium having computer-usable program code means embodied in the medium. Any suitable computer readable medium may be utilized including hard disks, CD-ROMs, optical storage devices, or magnetic storage devices.

Computer program code for carrying out operations of the present invention may be written in an object oriented programming language such as Java®, Smalltalk or C++. However, the computer program code for carrying out operations of the present invention may also be written in conventional procedural programming languages, such as the “C” programming language.

The program code may execute entirely on the user's computer, as a stand-alone software package; on a remote computer; or it may execute partly on the user's computer and partly on a remote computer. In the latter scenario, the remote computer may be connected directly to the user's computer through a LAN or a WAN (Intranet), or the connection may be made indirectly through an external computer (for example, through the Internet using an Internet Service Provider). The invention may be implemented as software that may be resident on a stand-alone device such as a personal computer, a PAL device, a personal digital assistant (PDA), an e-book or other handheld or wearable computing devices (incorporating Palm OS, Windows CE, EPOC, or future generations like code-named products Razor from 3Com or Bluetooth from a consortium including IBM and Intel), or a specific purpose device having an application specific integrated circuit (ASIC).

The present invention is described below with reference to flowchart illustrations of methods, apparatus (systems) and computer program products according to an embodiment of the invention. It will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. Examples of how the software can be stored for use are the following: in random access memory (RAM); in read only memory (ROM); on a storage device like a hard drive, disk, compact disc, punch card, tape or other computer readable material; in virtual memory on a network, a computer, an intranet, the Internet, the Abilene Project, or otherwise; on an optical storage device; on a magnetic storage device; and/or on an EPROM.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

The present invention involves a method for predicting cognitive performance at a given time in the past, present, or future as a consequence of the amount of sleep and wakefulness up to that time as a function of the time of day and the workload for a particular individual. The method calculates a numerical estimate of cognitive performance for an individual as a continuous function of time. The calculations (described below) are based on empirically derived direct mathematical relationships among (1) the continuous decrement of cognitive performance during wakefulness; (2) restoration of cognitive performance during sleep; (3) cyclic variation in cognitive performance during the course of the day; and (4) variations in cognitive performance due to whether and what activities are occurring.

In accordance with the invention, a numeric value indicating predicted cognitive performance at a given moment in time is provided as shown in FIGS. 1( a)-4(b). As shown in FIG. 1( a), predicted cognitive performance equals the output of a series of calculations and/or determinations obtained in three general steps, using functions empirically derived from direct measurements of cognitive performance under scientifically controlled conditions. The first step, as shown in FIG. 2, preferably uses a set of functions to calculate an initial value referred to as the level of cognitive performance capacity as graphically depicted in FIGS. 3( a)-(c). Once the level of cognitive performance capacity is calculated, the second step preferably calculates or uses a previously calculated time of day modulator M represented as G8 in FIG. 1( b) and S8 in FIG. 4( b). The third step preferably calculates a task modulator T represented as S9-S10(b) in FIG. 4( b). Alternatively, the second and third steps may be switched and/or combined. The fourth step preferably involves the mathematical combination of the results from the first through third steps yielding a predicted cognitive performance, shown as a block diagram in FIG. 1( a) and graphically represented in FIG. 1( b), which illustrates the combination of the cognitive performance capacity and the time of day modulator.

There are four functions relating to the sleep/wake history used to calculate the level of cognitive performance capacity as shown in FIGS. 2-4( b). The wake function w(t) quantifies empirically derived relationships between the time awake and degradation of cognitive performance. The sleep function s(t) quantifies empirically derived relationships between the time asleep and maintenance and/or recuperation of cognitive performance. In addition to these two primary functions that operate during the bulk of the time awake or asleep there are two other functions that operate briefly during the transition from one state to the other. They include the delay of recuperation function d(t) and the sleep inertia function i(t). The delay of recuperation function d(t) represents the relationship between the wake to sleep transition and the recuperation of cognitive performance. This function operates during the initial period of sleep following being awake, known as stage 1 sleep, as shown in FIG. 3( b). The sleep inertia function i(t) represents the relationship between the sleep to wake transition and cognitive performance. This function operates during the initial period of time being awake after being asleep as shown in FIG. 3( c).

The function representing the time of day effects on cognitive performance is used to calculate a modulating factor M. The time of day function describes empirically derived relationships between the time of day (point in time within a 24-hour period) and the variation in cognitive performance over the course of the day as exemplified by G8 in FIG. 1( b).

The function representing the task/activity impact on cognitive performance is used to calculate a modulating factor T. The task function describes the impact of the performance of a task and/or an activity upon cognitive performance preferably based upon, for example, the intensity, the length, the complexity, and the difficulty associated with the particular task and/or activity. FIG. 5 illustrates the impact of performing a task across a 10-minute Psychomotor Vigilance Task (PVT) session at two hour increments during 40 hours of total sleep deprivation. For each PVT session, except the last one, there was an improvement from trial 10 of one PVT session to trial 1 of the next PVT session.

A mathematical operation, shown in FIG. 1( b) as multiplication, is used to combine the results from the first, second, and third steps into a single predicted cognitive performance curve E in the fourth step.

Using the preferred embodiments, predicted cognitive performance E can theoretically reach an index level of 120, but only when cognitive performance capacity C is an index level of 100 (i.e., 20 minutes after awakening from a sleep period in which cognitive performance capacity C was fully restored) and simultaneously the time of day function M is at its acrophase. Although possible, in practice this situation is unlikely.

The inputted data S2 into the method includes a representation of an individual's sleep/wake history and task information. The sleep/wake history is a time series or temporal record based on local clock time. Each successive period, interval or epoch identifies one of two mutually exclusive states, namely wake or sleep. The task information is a series of information regarding what the individual is or is not performing in the way of activities/tasks and alternatively the intensity, the difficulty, the length and/or the complexity of the activity/task may be included in the task information. Both the sleep/wake history and the task information are not necessarily “historical” in the sense of occurring in the past, but may for example be hypothetical, projected, idealized, or contemplated. The latter in particular are appropriate for the predictive uses of this method.

The gold standard for measuring sleep and wakefulness is polysomnography (PSG). PSG sleep scoring is based on the concurrent recording, or at least recording in such a way as allows the latter synchronization (typically with time-stamping or time-linking) of the data, of electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). These signals are typically then visually inspected on an epoch-by-epoch basis (each epoch traditionally is 30 seconds in length for PSG) to determine an individual's stage of sleep or wakefulness. Polysomnographic sleep scoring distinguishes between wake, non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), with NREM sleep being further distinguished into four stages (stages 1, 2, 3, and 4) on the basis of characteristic EEG markers. PSG is not a practical method for determining sleep and wakefulness in applied settings (e.g., while driving, working, or on the battlefield), because PSG requires that individuals be attached to sensors or electrodes that connect with a recording device, and currently the only accepted method for scoring PSG is by visual inspection of the recorded EEG, EOG, and EMG results.

Presently, if a computer is used for scoring PSG, then typically a human reviews the results for accuracy in the scoring, because computer scoring has not been approved by the American Sleep Disorders Association. Also, recently, researchers have been exploring whether spectrally analyzed PSG or similar data using Fast Fourier Transforms might provide a better measurement of sleep in humans than PSG scoring.

A preferred method of determining sleep from wakefulness would be a device that is portable, unobtrusive, reliable, and whose recordings can be scored automatically. One such method is monitoring of movement activity, or actigraphy. The movement activity device is typically worn on the non-preferred wrist, but may be placed elsewhere on an individual (e.g., the ankle). When worn on the non-preferred wrist, these devices have been shown to accurately quantify sleep and wakefulness as compared to the standard provided by PSG (reliabilities as high as 90%).

The most widely used method of scoring actigraphy data is an algorithm developed by Cole and associates and described in their article entitled “Automatic Sleep/Wake Identification from Wrist Actigraphy” published in Sleep, vol. 15, pp. 461-469 (1992). Successful actigraphy sleep-scoring algorithms such as the Cole et al. algorithm (also known as the Cole-Kripke algorithm) are for use with conventional (number-of-zero-crossings) actigraphs, and some algorithms account for the number of counts above a certain threshold. These algorithms are limited to making simple sleep vs. wake distinctions, and cannot distinguish sleep stage changes (e.g., Stage 1 to Stage 2, or Stage 2 to REM) within sleep itself. Consequently, such algorithms cannot discriminate recuperative sleep (stages 2, 3, 4, and REM) from non-recuperative sleep (stage 1).

More recently, digital signal processing (DSP) actigraphs have begun to be developed. Because the DSP actigraph will provide much more information than just the conventional number of zero crossings or counts above threshold (this and other information provided by a conventional actigraph will, however, be retained), it shows promise for distinguishing between different sleep stages. Thus, sleep scoring systems for DSP will not only replace, but will also make irrelevant, the Cole-Kripke algorithm. A sleep scoring system for the DSP will be developed as the DSP database of empirical data from use of DSP actigraphs increases.

Other algorithms and methodologies for automated actigraphy scoring have been developed by, for example, Jean-Louis et al., 1996; Sadeh et al., 1989; and Zisapel et al., 1995. Each of these scoring systems shows considerable promise, especially for scoring the actigraphically recorded sleep/wake states of individuals with sleep disorders or other medical disorders. Available scoring systems mainly differ along technical aspects, for example, the extent to which activity counts in previous and subsequent epochs influence the scoring of the current epoch; and variation among mathematical principles underlying each scoring system. As one of ordinary skill in the art will realize, any actigraph scoring system is capable of providing the sleep/wake data input for the method of this invention.

The sleep/wake history will preferably take the form of a data series. The sleep/wake history may include past, present, and/or future (predicted) sleep/wake patterns of an individual. The sleep/wake history is a representation of a state of an individual as either being asleep or awake and is divided into epochs. The epochs are the same length, but that length could be of any time period as dictated by restraints of the method and apparatus used to collect data and/or the desired precision of the sleep/wake pattern. The PSG or similar scoring can be converted into a sleep/wake history for an individual.

It can be appreciated that the accuracy of the cognitive performance prediction is directly related to the accuracy of the sleep/wake history input and the sleep scoring system used to interpret the sleep/wake states of an individual. One possible source of inaccuracy may arise from the temporal resolution of the input epoch or interval. That is, the shorter the input epoch, the higher the temporal resolution and consequent moment-to-moment accuracy of the sleep/wake input. For example with actigraphy, past experience indicates that the most effective length of an epoch is one minute. Another source of inaccuracy may arise from ambiguity in the sleep/wake discrimination itself. In the event that the history input is ambiguous (i.e., the sleep or wake state is uncertain), the calculation of predicted cognitive performance can be performed twice concurrently, once for each possible state (sleep or wake), resulting in a dual output representing the possible range of expected cognitive performance. One of ordinary skill in the art will appreciate that the dual output can be further divided if there is more than one ambiguity in the sleep/wake history. Such treatment in executing the functions expressed below is included as a component of this method and any implementing apparatus.

The method of this invention is not limited with regard to time or technique: on-line/real-time vs. off-line/post-hoc; or incremental, iterative vs. discrete solutions to continuous forms of those equations.

A preferred embodiment of the method encompasses a mathematical model that expresses predicted cognitive performance capacity E at time t as a modulation of the current cognitive performance capacity C by a time of day function M by a task function T. It can be written as a general description in its simplest form as: E=C∇M∇T  Equation 1 where ∇ represents a mathematical operator. Any mathematical operator may be used to combine cognitive performance capacity C, day of time function M, and task function T. The form and nature of time of day function M and/or task function T dictate the exact operator that is most desirable. There may be two different operators used to express the predicted cognitive performance capacity E such that the first ∇ may be one mathematical operator and the second ∇ may be a second mathematical operator. Alternatively, the modulations could be performed in two steps where two of the items are modulated with the resulting modulated value being modulated with the third of the items. Most preferably, Equation 1a below would be used to combine cognitive performance capacity C, day of time function M, and task function T. E=C*M*T  Equation 1a In the alternative, Equation 1b below could also be used to combine cognitive performance capacity C, time of day function M, and task function T. E=C+M+T  Equation 1b

Cognitive performance capacity C represents a function of sleep/wake history, that is C=w(t)+s(t)+d(t)+i(t)  Equation 2 where w(t), s(t), d(t), and i(t) are the instantaneous values of the wake, sleep, delay, and sleep inertia functions at time t. Time of day function M represents a function of the time of day, such that M=m(t)  Equation 3 where m(t) is the instantaneous value of the time of day function at time t. Task function T represents a function of the impact of performing or not performing a task when the individual is awake, such that T=t(t)  Equation 4

In keeping with the invention, a four-step process may be performed after either an initial setting of the starting time t, the starting cognitive performance capacity C, and the time of the last transition t_(LS) when appropriate in S1 of FIG. 4( a) where these data can be entered in any order. In the first step, the level of cognitive performance capacity C at time t may be calculated based on an individual's sleep/wake history using functions w(t), s(t), d(t), and i(t) as represented by S3-S7 e in FIGS. 4( a)-(b). In the second step, time of day modulator M may be calculated using the time of day function as represented by S8 in FIG. 4( b). According to an aspect of the invention, the second step can be performed once to provide a series of data points in time sequential order for multiple executions of the first step. In the third step, task modulator T may be calculated using the task function as represented by S9 a through S10 c in FIG. 4( b). In the fourth step, predicted cognitive performance E may be derived from the combination of cognitive performance capacity C and time of day modulator M resulting in cognitive performance capacity C being modulated by time of day modulator M being modulated by task modulator T as illustrated by S11 in FIG. 4( b).

First Step: Calculation (or Determination) of Cognitive Performance Capacity C

FIG. 2 is a schematic flow diagram representing the use of the functions described below. Examples of the calculations discussed are graphically illustrated in FIGS. 3( a)-(c). FIGS. 4( a)-(b) are a detailed flowchart of the steps in the method. As a preferred embodiment of the model, cognitive performance capacity C is herein assigned index values preferably having a total range of zero to 120. The ranges in this application are intended to encompass the end points of the stated numerical range. However, cognitive performance capacity C may be scaled to other values or units for specific applications, for example, zero to 100.

In the preferred embodiment, only one of the four functions w(t), s(t), d(t), and i(t) operates at any given interval of time, and the others are equivalent to zero in Equation 2 as represented by S7 a through S7 d. Functions w(t) and s(t) describe the non-transition states, while functions d(t) and i(t) describe the transition states. For instance in a non-transition state, when the individual is awake, function s(t) is set to zero, and when the individual is asleep, function w(t) is set to zero. Likewise, during specific intervals of transition from wake to sleep and vice versa, only one of the transition functions d(t) or i(t) operates, the other being set equal to zero. When there is a change between sleep and wake, or vice versa, a time counter t_(LS) is reset to keep track of the time in the present state for determining decision rules for the transition functions d(t) and i(t) as shown in FIG. 4( b).

(1) Wake Function (w(t))

The wake function S7 a represents the depletion of cognitive performance capacity with the passage of time awake. It is based on evidence that (1) near-100% cognitive performance is maintained from day to day when individuals obtain eight hours of sleep each night; and (2) cognitive performance appears to decline by approximately 25% for every 24 hours of wakefulness.

In S7 a, the wake function w(t) calculates the current value of cognitive performance capacity C resulting from the decay in cognitive performance capacity that occurs over an interval of time from t−1 to t, which in the preferred embodiment is the length of one epoch. As noted above, this calculation is performed independent of and prior to modulation of cognitive performance capacity C by the time of day function M in S9. A generalized form of the wake function is given by the equation: C _(w) =w(t)  Equation 5 where wake function w(t) may be any positive-valued function decreasing with t. More preferably, the wake function w(t) is a linear function depleting performance at a constant rate, and, most preferably, the wake function w(t) is expressed at time t as follows: w(t)=C _(t−1) −k _(w)  Equation 5a where the interval of wakefulness is from t−1 to t (in epochs) and the decay in performance per minute is k_(w). Thus, if t−1 to t is not one minute, then k_(w) is appropriately adjusted. The total range of k_(w) is any positive real number, and preferably k_(w) is a range of 0.003 to 0.03 of a point per minute, and most preferably k_(w) is equal to approximately 1 point per hour or 0.017 of a point per minute. The value k_(w) is based on empirical data showing that cognitive performance declines by approximately 25 points for every 24 hours of continuous wakefulness. Equation 4a is represented in FIGS. 2 and 4( b) at S7 a. An example is illustrated as the wake function in FIG. 3( a), for an initial cognitive performance capacity of 100 index points, a decay rate of 0.017 of a point per minute, over an interval of 16 hours (960 minutes). (2) Sleep Function (s(t))

The sleep function S7 c restores cognitive performance capacity with the passage of time asleep. The sleep function s(t) is based on empirical evidence that the recuperative value of sleep on cognitive performance accumulates in a nonlinear manner. That is, the rate of cognitive performance capacity recuperation is higher initially during sleep and slows as the time asleep accumulates. Other data indicates that sleep beyond a certain point confers little or no additional benefit for cognitive performance and the rate of recuperation approaches zero. Thus, for example, two hours of sleep are not twice as recuperative as one hour of sleep. The sleep function increases cognitive performance capacity at a rate that depends on the current level of cognitive performance capacity—the lower the initial cognitive performance capacity, the more rapidly recuperation accumulates. In other words, preferably the slope of a tangential line for a particular cognitive performance capacity index level is the same each time that index level is reached during different sleep periods.

For example, following a full day (16 hours) of wakefulness, during ensuing nighttime sleep recuperation accumulates rapidly early in the night. As cognitive performance capacity is restored across the sleep period, the rate of recuperation declines. Following sleep deprivation, initial cognitive performance capacity is even lower than it would be following a normal 16-hour day, and the rate of recuperation is even higher than at the beginning of recovery sleep. During chronic partial sleep deprivation, cognitive performance capacity may not be completely restored each night despite this more rapid initial recuperation rate.

The sleep function calculates the current value of cognitive performance capacity C resulting from the recovery of capacity that occurs while an individual is asleep over an interval of time T (from t−1 to t). As noted above, this calculation is performed independent of, and prior to, modulation of C by the time of day function M and modulation by the task function T. A generalized form of the sleep function is given by the equation: C _(s) =s(t)  Equation 6 where sleep function s(t) may be any positive-valued function increasing with t, and more preferably the sleep function s(t) is an exponential function. This is based on empirical data showing that cognitive performance restoration during sleep is nonlinear, with the rate of recuperation highest initially and gradually slowing as sleep continues. Thus, the most preferred sleep function is an exponential function, which in its discrete form is stated as: C _(t) =C _(t−1)+(100−C _(t−1))/k _(s)  Equation 6a where the interval of sleep is from t−1 to t (in minutes), the maximum cognitive performance capacity value is 100 index points, C_(t−1) is cognitive performance capacity in the period preceding time t, and k_(s) is the recuperation “time constant”. In other words, k_(s) is the time required to fully restore cognitive performance capacity C if it was restored at a constant rate equal to the initial slope of the curve. The recuperation time constant k_(s) is derived empirically from partial sleep deprivation data and is selected based on the length of the epoch. In accordance with the preferred embodiment, k_(s) is equal to any positive real number. For example, k_(s) may be in the range of 100 to 1000 with an epoch length of one minute, and, more particularly may be approximately 300 with an epoch length of one minute. However, the optimum values for k_(s) will depend at least in part on the length of the epoch. Equation 6a is represented in FIGS. 2 and 4( b) as S7 c. A graphical example is illustrated as the sleep function in FIG. 3( a), using an initial cognitive performance capacity level of 100 index points, and using a time period of one minute and k_(s)=300, the effect of eight hours of sleep following 16 hours of wakefulness. (3) Delay Function d(t) for Wake to Sleep Transitions

The delay of recuperation function d(t) defines the duration of an interval after sleep onset during which recuperation of cognitive performance capacity from the sleep function is delayed. During this interval, the wake function degradation of cognitive performance capacity continues as represented by S7 d in FIG. 4( b). By preventing immediate accumulation of cognitive performance capacity at the beginning of a sleep period or following awakenings from sleep, this delay adjusts the cognitive performance capacity calculation S6 b.

The delay of recuperation function is based upon empirical studies showing that the first few minutes of sleep are generally comprised of stage 1 sleep, which is not recuperative for sustaining cognitive performance capacity. Frequent arousals to wake or stage 1 sleep (sleep fragmentation) drastically reduce the recuperative value of sleep on cognitive performance capacity. Available data suggest that five minutes is the approximate length of time required to return to recuperative sleep (stage 2 or deeper sleep) following an arousal to wake or stage 1 sleep. If many hours of sleep are obtained without interruption, then the delays make only a small difference in overall restoration of cognitive performance capacity. If sleep is interrupted with frequent awakenings, the delays in recuperation after each awakening will accumulate, and thus substantially reduce total cognitive performance capacity restored during the total sleep period.

The delay function specifies the duration of a sleep interval during which application of the sleep function is postponed and a transitional formula is applied. A generalized form of the delay function for wake to sleep transitions is expressed as a decision rule: d(t):IF(t−t _(LS))≦k _(d)THENC _(t) =d(t)ELSEC _(t) =s(t)  Equation 7 where LS stands for last state change, and thus the wake to sleep transition time t_(LS) denotes the time of the last wake interval preceding a contiguous series of sleep intervals. This decision rule is shown in FIGS. 2 and 4( b) as S6 b, S7 c and S7 d taken together. For calculating cognitive performance capacity during the interval k_(d), cognitive performance capacity C_(t) is evaluated by a transitional formula C_(t)=d(t). After k_(d) has elapsed, C_(t)=s(t). Note that if wakefulness ensues before the end of k_(d), then C_(t) never reverts to s(t). That is the sleep function is not applied during the brief sleep interval.

It is believed that the preferred range for k_(d) is from 0 to 30 minutes, more preferably k_(d) equals about five minutes measured from the time of sleep onset before recuperation is derived from sleep. Preferably d(t) equals w(t). One of ordinary skill in the art will realize there are a variety of factors that influence the length of k_(d). Thus a more preferred delay function may be expressed as: d(t):IF(t−t _(LS))≦5THENC _(t) =w(t)ELSEC _(t) =s(t)  Equation 7a The effects of delayed recovery on cognitive performance capacity, as embodied by Equation 7a, are graphically illustrated in detail in FIG. 3( b).

As one of ordinary skill in the art will appreciate, PSG or similar scoring is able to classify when stage 1 sleep occurs. The conversion of PSG or similar scoring data would then convert the occurrences of stage 1 sleep into wake data for the sleep/wake history. Consequently, when the sleep/wake history is based on converted PSG or similar scoring data, the delay function d(t) is not necessary for the determination of an individual's cognitive performance capacity. Alternatively, the delay function could be determined based upon when the individual entered stage 2 or deeper sleep instead of using the k_(d) value, and that once stage 2 or deeper sleep is reached then the sleep function s(t) would be used.

Alternatively, the delay function d(t) may simply maintain the cognitive level of C_(t) at the beginning of the delay period, i.e., C_(tLS).

(4) Sleep Inertia Function i(t) for Sleep to Wake Transitions

The sleep inertia function i(t) defines the duration of an interval after awakening from sleep during which manifest cognitive performance capacity is suppressed below the actual current level. The sleep inertia function i(t) is based upon empirical data showing that cognitive performance is impaired immediately upon awakening, but improves primarily as a function of time awake. It is also based on positron emission tomography studies showing deactivated heteromodal association cortices (those areas that mediate this cognitive performance) immediately upon awakening from sleep, followed by reactivation of these areas over the ensuing minutes of wakefulness. That is, actual cognitive performance recuperation realized during sleep is not apparent immediately after awakening. The data indicate that 20 minutes is the approximate length of time required for cognitive performance capacity to return to levels that reflect actual recuperation accrued during sleep.

A sleep inertia delay value k_(i) specifies the duration of the interval after awakening during which manifest cognitive performance capacity may be transitionally suppressed below the sleep-restored cognitive performance capacity level. During this interval, a transitional function bridges from an initial level to that determined by the wake function alone. A generalized form of the sleep inertia function for sleep to wake transitions is expressed as a decision rule: i(t):IF(t−t _(LS))<k _(i)THENC _(t) =i(t)ELSEC _(t) =w(t)  Equation 8 where the sleep to wake transition time t_(LS) denotes the time of the last sleep interval preceding a contiguous series of wake intervals. For calculating cognitive performance capacity during the interval k_(i), C_(t) is evaluated by a transitional formula C_(t)=i(t). After k_(i) has elapsed, C_(t)=w(t). Equation 8 is represented in FIGS. 2 and 4( b) as S6 a, S7 a and S7 b taken together.

The preferred range for k_(i) is from 0 to about 60 minutes, and preferably in the range of about 10 to about 25 minutes, and most preferably between 18 and 22 minutes.

The sleep inertia function i(t) may be any function over the interval 0 to k_(i), preferably any negatively accelerated function. A preferred sleep inertia function i(t) is a simple quadratic equation. This function preferably suppresses cognitive performance capacity by 10% to 25% immediately upon awakening, and most preferably by 25%. The function recovers 75% of the suppressed cognitive performance capacity in the first 10 (or about half of k_(i)) minutes after awakening and 100% of the suppressed cognitive performance capacity usually by 20 minutes after awakening, after which the wake function resumes. These values are based on empirical data concerning the transition from sleep to wake. These studies show that cognitive performance is impaired immediately upon awakening from sleep, that the bulk of this impairment dissipates within the first few minutes of awakening, and that approximately 20 minutes is required for performance to be fully restored. Using the preferred 25% suppression of cognitive performance capacity and 20 minute recovery time, the preferred form of the sleep inertia function is expressed as a decision rule: i(t):IF(t−t _(LS))<20THENC _(t) =C _(SW)*[0.75+0.025(t−t _(LS))−(0.025(t−t _(LS)))²]ELSEC _(t) =w(t)  Equation 8a where C_(SW) is cognitive performance capacity at the end of the sleep period calculated by the sleep function at the sleep to wake transition time t_(LS). This decision rule is shown in FIGS. 2 and 4( b) as S6 a, S7 a, and S7 b taken together. Equation 8a illustrates an initial suppression of 25% and k_(i) equal to 20 minutes, and a negatively accelerated ramp bridging the interval until the wake function w(t) resumes its effects. The effect of the sleep inertia function i(t) on cognitive performance capacity, as embodied by Equation 8a, is graphically illustrated in FIG. 3( c).

An alternative variant of the sleep inertia function i(t) is a linear equation based on k_(i) equal to 10 minutes and an initial 10% decrease in cognitive performance capacity. The resulting decision rule is then: i(t):IF(t−t _(LS))<10THENC _(t) =C _(SW)*[0.9+(t−t _(LS))/100]ELSEC _(t) =w(t)  Equation 8b

As one of ordinary skill in the art will realize, both Equations 8a and 8b can be adjusted for a change in the value of k_(i) and amount of the initial suppression of cognitive performance capacity.

Second Step: Calculation of the Time of Day Modifier M

(1) Time of Day Function m(t)

The time of day function m(t) shown at S8 in FIG. 4( b) describes the cyclical 24-hour variation in cognitive performance. The time of day function m(t) is based on empirical data showing that under constant routine and/or total sleep deprivation conditions (i.e., with sleep/wake history controlled), cognitive performance capability oscillates between approximately 5% to approximately 20% peak to peak over a 24-hour period. This effect is commonly attributed to circadian rhythms of individuals. Output from this function modulates the current cognitive performance capacity prediction C (calculated in the first step) according to the time of day. The result of this modulation is the predicted cognitive performance capacity E. A generalized form of the time of day function is given by M=m(t)  Equation 9 where m(t) can be any rhythmic function with a base period of 24 hours, and, preferably, m(t) is the sum of two sinusoids, one with a period of 24 hours and the second with a period of 12 hours, which provides a biphasic circadian component. This function may be based on empirical data showing that a considerable proportion of variability seen in cognitive performance measurements can be accounted for by two such sinusoidal waveforms. As previously noted, the peak in empirically observed cognitive performance capacity occurs usually between 8:00 PM and 10:00 PM, and the trough occurs usually between 2:00 AM and 6:00 AM, providing a variation of approximately 5% to approximately 20% each day. A secondary trough occurs usually around 3:00 PM. Using these values for the preferred form of function m(t), the resulting function accounts for the empirically demonstrated asymmetry of daily cognitive performance rhythms, with a mid-afternoon decrease.

The descriptive form of the function m(t), including its offset and amplitude values varies with the operator selected for the third step. The computed value of the function can be expressed either as an additive percentage of cognitive capacity (dependent or independent of the current value of cognitive performance capacity C_(t)) or as a multiplicative dimensionless scalar. The preferred form of the function, using the multiplicative operator, is expressed as m(t)=F+(A ₁*cos(2Π(t−V ₁)/P ₁)+A ₂*cos(2Π(t−V ₂)/P ₂))  Equation 9a where F is an offset, t is the time of day, P₁ and P₂ are periods of two sinusoids, V₁, and V₂ are the peak times of day in time units or epochs past midnight, and A₁ and A₂ are amplitudes of their respective cosine curves. This function may be used to modulate the previously calculated cognitive performance capacity C. Equation 9a is shown as S8 in FIGS. 1( a) and 4(b) and graphically illustrated as G8 in FIG. 1( b). As shown in FIG. 4( b), t is an input in the time of day function m(t) for each epoch of data.

For example in a preferred embodiment the variables are set as follows: t is the number of minutes past midnight, P₁ is equal to 1440 minutes, P₂ is equal to 720 minutes, V₁ is equal to 1225, and V₂ is equal to 560. Further, when A₁ and A₂ are represented as scalars, their amplitudes are in a range from 0 to 1, and more preferably are in a range from 0.01 to 0.2, and most preferably A₁ is equal to 0.082 and A₂ is equal to 0.036. Further in this example F is equal to either 0 or 1, and more preferably F is equal to 1. The resulting value of the time of day function m(t), in this example, is in the range of 0 to 2, and preferably in the range of 0.8 to 1.2, and most preferably in the range of 0.92 to 1.12.

As mentioned above, the second step may, for example, be preformed on the fly, for example, in real time or be previously calculated prior to the first step.

Third Step: Calculation of the Time on Task Modulator T

In the preferred embodiment, only one of the two functions g(t) and h(t) operates during any period in which the individual is awake with the other function being equivalent to zero. However, when the individual is asleep then both functions g(t) and h(t) are equal to zero as represented by S9 a through S10 c in FIG. 4( b). The selection of the function preferably is based upon whether the individual is performing a task or not is illustrated by S9 b through S10 b and the individual is awake is illustrated by S9 a and S10 c. As such, the time on task modulator may be calculated prior to (as illustrated in FIG. 8( a)) or after steps S7 a and S7 b instead as a separate branch as illustrated in FIG. 4( b).

(1) Rest Function g(t)

The rest function g(t) is illustrated as S10 a in FIG. 4( b). The rest function g(t) preferably represents the restoration of cognitive performance capacity due to an individual resting and relaxing between tasks and/or activities. The rest function g(t) preferably does not provide the same amount of restoration that occurs during sleep as discussed above with respect to the sleep function s(t). A generalized form of the rest function is given by the equation: t(t)=g(t)  Equation 10 where g(t) may be any positive-valued function. Alternatively, the rest function g(t) may be expressed as follows: g(t)=z*s(t)  Equation 10a where z is a scalar, which preferably is in a range of 0 to 1, and t_(LS) will preferably represent the length of the resting and/or inactivity period. (2) Work Function h(t)

The work function h(t) is illustrated as S10 b in FIG. 4( b). The work function h(t) preferably represents declination of cognitive performance capacity due to an individual performing a task(s) and/or an activity(ies). In S10 b, the work function h(t) calculates the task modulator T resulting from the decay in cognitive performance capacity that occurs over an interval of time from t−1 to t, which in the preferred embodiment is the length of one epoch. A generalized form of the work function is given by the equation: t(t)=h(t)  Equation 11 where h(t) may be any negative-valued function decreasing with t. More preferably, the work function h(t) is a linear function depleting performance at a constant rate. Alternatively, the work function h(t) may be an exponential function that, for example, may increase the depletion rate the longer the task is performed and/or activity is done. Another or further alternative is that the type of task, i.e., the difficulty, the complexity, and/or the intensity will impact the depletion rate per epoch. The greater the difficulty, the complexity, and/or the intensity of the task, then the greater the depletion rate per epoch will be.

Alternatively, both or just one of the rest function and the work function may be impacted by the time of day modulator M such that prior to being modulated with the cognitive performance capacity C and the time of day modulator M, the task modulator T is modulated based upon the time of day as represented by the time of day modulator M. A further alternative is for the time of day modulator M to be used twice in Equations 1, 1a, and 1b above.

(3) Asleep Function

A generalized form of the task function when the individual is asleep is t(t)=1  Equation 12 where the modulation is performed using multiplication, because the task function T will not impact the individual's cognitive performance index. Alternatively, if the task modulator is added to the other functions, then the task function will take the following form t(t)=0  Equation 12a Fourth Step: Calculation of Predicted Cognitive Performance

The overall process of calculating predicted cognitive performance capacity E is illustrated schematically in FIGS. 1( a) and 4(a)-(b). The time of day function M and the task function T modulate the cognitive performance capacity C derived from the individual's sleep/wake history to generate the final predicted cognitive performance E as shown in, for example, FIG. 1( a). In the third step, predicted cognitive performance E is derived from the combination of cognitive performance capacity C, time of day function M, and task function T. In its most general form: E=C∇M∇T  Equation 1 where ∇ is any mathematical operation for combining cognitive performance capacity C, time of day function M, and task function T. The conventional choice of operations for providing this combination is addition or multiplication. Depending on the form of time of day function m(t) and task function T(t) selected above, the same numerical value of predicted cognitive performance E can be generated by either operation. Most preferably the combination is performed with multiplication S11, represented as: E=C*M*T  Equation 1a In Equation 1a, the predicted cognitive performance E is the modulation of the current cognitive performance capacity C with a value centered around the number one representing the current value of the time of day modulator M and the task modulator T.

As noted above, the preferred numerical representation of cognitive performance capacity C is a value ranging from zero to 100 to represent an index (or a percentage) of cognitive performance capacity available for a particular individual. However, predicted cognitive capacity E can meaningfully exceed 100 under certain circumstances due to time of day modulation about the current value of cognitive performance capacity C. A possible example of such a circumstance would be a sleep period resulting in an index level of 100 cognitive performance capacity C and terminated at the evening peak (and after sleep inertia has dissipated).

Alternatively, if a percentage representation is used while retaining a 100% scale, either the predicted cognitive capacity E may be truncated/clipped at 100% or 0 to 120% may be scaled to 0 to 100%. Either choice will maintain a maximum of 100%. This most likely will be implemented as scaling 120% to 100% and then truncating/clipping any predicted cognitive capacity E to 100% if the prescaled value exceeds 120%.

As shown in FIG. 1, the method repeats for each new epoch of data. For each iteration of the method, one time unit equal to the length of an epoch may be added to time t preferably in the form of a counter S13 as exemplified in FIG. 4( b). The counter step S13 may occur, for example, as illustrated in FIG. 4( b), at the same time as S11 or S12, or after S12.

In the preferred embodiment described above, the sleep inertia function i(t) is applied to cognitive performance capacity C prior to modulation of cognitive performance capacity C by the time of day modulator M and/or task modulator T. An alternative embodiment applies the sleep inertia function i(t) not to cognitive performance capacity C, but to predicted cognitive capacity E, that is, subsequent to the modulation of cognitive performance capacity C by time of day modulator M and/or task modulator T.

Also in the preferred embodiment described above, the wake function w(t) is set to zero when the sleep inertia function i(t) is applied. Another alternative embodiment applies the sleep inertia function i(t) and the wake function w(t) simultaneously. When the sleep inertia function i(t) and the wake function w(t) become equal to each other or the sleep inertia function i(t) becomes greater than the wake function w(t), then cognitive performance capacity C is calculated (or determined) using the wake function w(t).

The preferred embodiment may be further modified to account for the effects of narcotics or other influences that will impact the cognitive capacity as shown in FIG. 6. Further modification to the preferred embodiment will allow for the inclusion of jet lag and similar time shifting events by, for example, compressing or expanding the 24 hour period of the time of day function M(t) over a period of days to realigning the time of day function M(t) to the adjusted schedule.

The preferred embodiment may be modified to include the testing of the individual at regular intervals to collect additional data and adjust the current cognitive performance index to reflect the results of the test. A test that may be used is the PVT or similar reaction time measurement test(s). The current cognitive performance index at the time of the test then is adjusted preferably along with the underlying weights of variables discussed above in connection to the Equations such that the method and/or apparatus is fine-tuned to reflect a particular individual's recuperation and/or depletion of cognitive performance capacity.

Another alternative embodiment is the removal of the third step from the preferred embodiment. Like the other alternative embodiments, this alternative embodiment may be combined in a variety of ways with the other alternative embodiments.

Implementation of the Method

The preferred embodiment may be realized as software to provide a real-time current state of an individual's cognitive performance and the capability upon demand to extrapolate future levels of cognitive performance. A flowchart representing the steps to be performed by the software in the preferred embodiment is shown in FIGS. 4( a)-(b) and for an alternative embodiment, to be described later, in FIGS. 8( a)-(b).

The software may be implemented as a computer program or other electronic device control program or an operating system. The software is preferably resident in a device, e.g. an actigraph, attached to the individual or in a stand-alone device such as a personal computer, a PAL device, a personal digital assistant (PDA), an e-book or other handheld or wearable computing devices (incorporating Palm OS, Windows CE, EPOC, or future generations like code-named products Razor from 3Com or Bluetooth from a consortium including IBM and Intel), a specific purpose device receiving signals from a device, e.g. an actigraph, attached to an individual or human input from human analysis or observation. The software could be stored, for example, in random access memory (RAM); in read only memory (ROM); on a storage device like a hard drive, disk, compact disc, punch card, tape or other computer readable material; in virtual memory on a network, a computer, an intranet, the Internet, the Abilene Project, or otherwise; on an optical storage device; on a magnetic storage device; and/or on an EPROM. The software may allow for the variables in the equations discussed above to be adjusted and/or changed. This capability will allow users to adjust the variables based on empirical knowledge and also learn the interrelationship between the variables.

The software implementation onto the measuring device such as an actigraph will convert any decimal numbers used in calculations into integers that are appropriately scaled as is well known to those skilled in the art. Further the integers would then be approximated such that minimal error would be created, for example, approximation for the Cole-Kripke algorithm weighting factors become 256, 128, 128, 128, 512, 128, and 128, respectively. Using linear approximation will simplify the binary arithmetic and the corresponding assembly code for software implementation.

In software, the time of day modulator would be embodied as a table with one hour steps resulting in 24 rows using 8-bit unsigned integers. The intervening steps would be interpolated from the one hour steps to provide 15-minute steps. This simplification provides sufficient resolution for available displays. A pointer system would be utilized to retrieve the appropriate data to calculate the time of day modulator. Depending on a myriad of factors, one of ordinary skill in the art will most likely choose a multiplicative modulation to achieve appropriate scaling or an additive modulation for less complex but more rapid evaluation, i.e., if speed is a concern. The main disadvantage with the additive modulation is that there will be an approximately 3% error compared to the 1% error using the multiplicative modulation in this invention. This system will allow the time of day function to be uploaded when the measuring/recording device, such as an actigraph, is initialized and reduce the repetitive computational burden that would exist if a cosine table was used and the time of day function was calculated from the cosine table for each epoch.

The preferred embodiment, as shown in FIG. 7( a), may also be realized by a stand-alone device or a component add-on to a recording device. The stand-alone device is separate from the device or other means of recording an individual's sleep history. In contrast, the component add-on to a recording device includes modifying the recording device to include the component add-on to provide one device that both records and analyzes an individual's sleep history.

A suitable stand-alone device includes a physical input connection, e.g., an input port (input means 20) to be physically connected to an input device, e.g., a keyboard, data entry device, or a data gathering device such as an actigraph. Alternatively, the physical connection may occur over an information network. Alternatively, the input port may be an interface to interact with a user. Alternatively, the physical input connection may be realized by a wireless communication system including telemetry, radio wave, infrared, PCS, digital, cellular, light based system, or other similar systems. The wireless communication system has an advantage in that it eliminates the need for a physical connection like cables/wires, plug-ins, etc. which is particularly convenient when monitoring a mobile subject. The data gathering or data entry device provides a sleep history that may include past, present and/or predicted/anticipated sleep patterns of an individual. Input means 20 embodies S1 for initial inputting of information and S2 for the continual or one-time loading of data depending upon the implementation selected.

The stand-alone device further includes a data analyzer (interpretation means 30). The data analyzer performs S3-S6 b. Interpretation means 30 analyzes the input data by performing different analysis functions. Interpretation means 30 compares the present input data to the last input data to determine if there has been a change from sleep to wake or wake to sleep; and if so, then set a time counter to the time for the last state, S3 and S4 a in FIG. 4( a). Interpretation means 30 also classifies the inputted data, as represented by S5 in FIG. 4, to then be able to select or generate at least one of the following calculation functions responsive to the composition of the input data: 1) wake function, 2) sleep function, 3) delay function, and 4) sleep inertia function as depicted by S6 a-S7 d in FIG. 4( b). Interpretation means 30 may be realized by an appropriately programmed integrated circuit (IC). One of ordinary skill in the art will realize that a variety of devices may operate in concert with or be substituted for an IC like a discrete analog circuit, a hybrid analog/IC or other similar processing elements.

The stand-alone device further includes a calculator (determination means 40). Determination means 40 may be implemented by appropriately programming the IC of the interpretation means or it may be implemented through a separate programmed IC. Determination means 40 calculates the cognitive performance capacity factoring in the sleep/wake history and the current state using the function selected by interpretation means 30, S7 a-S7 d in FIG. 4( b).

The interpretation means 30 and determination means 40 may be combined into one combined means or apparatus.

The stand-alone device further includes a first memory 60 that stores modulation data including a modulating data series or curve preferably representing a time of day curve. The stand-alone device further includes a second memory 50 that holds data for the creation of a data series or a curve representing cognitive performance capacity C over time t. The first memory 60 and the second memory 50 may be any memory or storage method known to those of ordinary skill in the art. The second memory 50 is preferably a first-in-first-out memory providing means for adding the value from the determination means 40 to the end of the data series or the curve. The first memory and the second memory may be combined as one memory unit. As one of ordinary skill in the art will realize there may be a memory to store the various intermediary values necessary for calculating cognitive performance capacity C and predicted cognitive performance E as required to implement this invention as either hardware or software.

The stand-alone device also includes, as a separate IC or in combination with one of the previously mentioned ICs, a modulator (modulation means 70) embodying S8-S9 shown in FIG. 4( b). Modulation means 70 receives the present cognitive performance capacity calculated by determination means 40 and calculates the time of day value from data stored in the first memory 60. Modulation means 70 modulates the first data series or curve (cognitive performance capacity) with the time of day value. The modulation preferably is performed by matching the timing sequence information relating to the data series or the curves based on the latter of midnight and the length of time from the initial input of data as preferably determined by the number of epochs and the initial starting time related to the first entered sleep/wake state. Modulation means 70 may modulate a series of cognitive performance capacity values with the time of day function if the second memory 50 exists to store the cognitive performance capacity values.

As is well known by one of ordinary skill in the art, a counter or other similar functioning device and/or software coding may be used in the stand-alone device to implement S11 shown in FIG. 4( b).

The stand-alone device may also include a display to show a plotted modulated curve representing the modulation result over time, as stored in a memory, e.g. a first-in-first-out memory, or a numerical representation of a point on the modulated curve at a selected time from the modulation means 70 representing the predicted cognitive performance E. The numerical representation may take the form of a gauge similar to a fuel gauge in a motor vehicle or the number itself. The stand-alone device, as an alternative or in addition to the display, may include a printer or communication port to communicate with an external device for printing and/or storage of a representation of predicted cognitive performance E.

The stand-alone device instead of having dedicated hardware may provide the storage space and processing power to execute a software program and accompanying data files. In this case, the stand-alone device may be a desktop computer, a notebook computer, or similar computing device. The software program handles the receiving of the data representing sleep history from an outside source through a communication port or via a computer network such as intranets and the Internet, and then performs the necessary analysis and processing of the method described herein. The storage space may be a memory in the form of computer readable material storing at least the time of day curve and possibly the input data, which may also be resident in the random-access-memory (RAM) of the computer given its temporary use. The input data and the resulting produced data indicating various cognitive performance levels of an individual may also be saved to a more permanent memory or storage than is available in RAM.

An alternative embodiment modifies the input port 20 to receive some form of raw data, i.e., prior to being sleep scored, representing sleep activity of an individual. In this embodiment, the interpretation means 30 would then sleep score the raw data as part of the data analysis performed by it. A third memory to store the weighting factors required for sleep scoring, if a table is used for them, else the sleep scoring function will implicitly include the weighting factors and the third memory will be unnecessary.

Another alternative embodiment provides for the interpretation means 30 to filter the sleep/wake data such that for the first k_(d) number of sleep epochs after a wake epoch are changed to wake epochs. In keeping with the invention, the filtering may be accomplished a variety of ways. The preferred way is to add a decision step prior to S3 in FIG. 4( a) such that if D_(SW) is a sleep epoch and t−t_(LS)≦k_(d), then S3-S6 a will be skipped and S7 a will occur. The result is that the decision rule represented as d(t) in Equation 6 above would be eliminated, and S6 b and S7 d would be unnecessary in FIGS. 4( b) and 8(b).

One of ordinary skill in the art will appreciate that the stand-alone device is broad enough to cover a computer/workstation connected to the Internet or other computer network. A user would transmit their sleep/wake history over such network to the stand-alone device for obtaining a predicted cognitive performance based on the transmitted data. The interface of the stand-alone device may allow the user to adjust the variables discussed above in connection with the method to learn the interrelationship between the variables and the predicted cognitive performance. Preferably, the range of allowable adjustment of the variables would be that of the respective ranges discussed in connection with each of the variables above.

The component add-on to the measuring device may have similar components to the stand-alone device described above and shown in FIG. 7( a). Preferably the component add-on is contained in one integrated chip to minimize the space needed to house it and/or is implemented as software as part of a designed measuring device. The input means 20 becoming, for example, a wire or other type of connector. However, the add-on component may include more than one electrical component, e.g., a memory chip and an IC. The component add-on may transmit the predicted cognitive performance to a remote device for further analysis.

The apparatus for accomplishing the third step is illustrated as part of FIG. 7( b). The additional components preferably include a task input means 20′ for receiving information regarding the task that may either be manually provided through some sort of data entry mechanism such as a keyboard, touch pad, a button or set of buttons, a touch screen or other similar mechanisms, or through analysis of the data collected by the attached device. Alternatively, the task input means 20′ may be a part of or similar to the input means 20. Preferably, a determination means 40′ for calculating the task modulator based on what is received from the task input means 20′. The determination means 40′ preferably is in communication with the modulation means 70′, which is the modulation means 70 with the added modulation of the task modulator. As with the device described in connection with FIG. 7( a), the various components of FIG. 7( b) may be consolidated into one or a series of combination components. Additionally, the components in FIGS. 7( a) and 7(b) may also not be directly connected but separated into different devices.

The alternative embodiment described above involving real-time adaptation of the above-described method may be implemented by the addition of a routine such that the individual is notified to press a button for recalibration on the recording device. Based upon the individual's response time, the individual's current level of cognitive performance is determined and adjustments are accordingly made to the above-described Equations to allow for the recent activity of the individual to lead to the determined cognitive performance.

Both the software and/or hardware are envisioned as being able to operate and function in real-time. For the purposes of this invention, real-time is understood to represent a continuous stream and analysis of cognitive performance level for each epoch of sleep/wake data entered. Thus, the software and/or hardware will both provide to an individual or some other entity the present cognitive performance level based on the data from the last entered epoch of sleep/wake data entered into either the software or hardware. Most sleep scoring systems make the sleep/wake determination based on data from epochs on either side of the epoch being analyzed. Consequently, there would be a delay in providing information to the user.

As one of ordinary skill in the art will appreciate from this description, the described method is able to accept a continuous stream of data from either individual epochs or groups of epochs. If blocks of time are entered, then after initial transitions the first few epochs are governed by the appropriate transition function with the appropriate time of solid sleep or wakefulness being used in the non-transition functions.

As a feature of the invention, the sleep/wake data may comprise the time at which a state change occurs from sleep to wake or wake to sleep. The sleep/wake data may also comprise the duration of the individual's wake state and the duration of the individual's sleep state. In order to generate the predicted cognitive performance curve, the sleep/wake data may be extrapolated and/or expanded into a series of individual epochs. As discussed above an epoch represents a predetermined length of time. Thus the sleep/wake data may be presented in conventional units of time or may be presented in epochs. For example, if the sleep/wake data was sleep for 10 epochs and wake for 3 epochs, in generating the cognitive performance capacities, epochs 1 through 10 may represent the sleep state and epochs 11 through 13 may represent the wake state.

In accordance with an aspect of the invention, the predicted cognitive performance E at a particular time q may be determined using either the predicted cognitive performance E or the cognitive performance capacity C at time r as a base point where r can be before or after time q. From the base point determining the cognitive performance capacities for the time points between times q and r where there is a change in state.

As shown in FIGS. 8( a)-(b), the steps are substantially the same as the preferred embodiment with changes made to the wake and sleep functions, consequently the definition of the variables is the same as the preferred embodiment except as noted. The equations described below and the steps shown in FIGS. 8( a)-(b) are for the situation when the initial cognitive value is prior in time to the desired predicted cognitive value. Each element of sleep/wake data is classified as either sleep or wake.

If the sleep/wake data represents the wake state, then the impact of the task function t(t) is determined. Alternatively, the task function t(t) may be modulated by the time of day function M prior to modulating the wake function wm(t) or the sleep inertia function i(t). Next, a selection is made between two functions as to which is applicable based on the following decision rule: IFΔt≦k _(i)THENC _(t) =i(t)ELSEC _(t) =w _(m)(t)  Equation 13 where Δt represents the amount of time in the current state, i.e., t−t_(LS). The sleep inertia function i(t) is used only if the last data entry is the wake state for a period of time is less than or equal to k_(i). Thus the same sleep inertia function i(t) as used in the preferred embodiment is also used in this alternative embodiment after being modulated by the task function t(t). The modified wake function w_(m)(t) takes into account that the sleep inertia function i(t) provides a delay of k_(i) when a curve is formulated, such that after an individual recovers from the initial suppression of cognitive performance capacity the individual returns to the level of cognitive performance capacity of the last epoch the individual was asleep prior to waking. Accounting for this delay provides the following: w _(m)(t)=C _(t−1) −k _(w)(Δt−k _(i))  Equation 14 Alternatively, the modified wake function w_(m)(t) may begin at a point where an undelayed w_(m)(t) intersects the sleep inertia function i(t). The wake function w_(m)(t) is modulated by the task function t(t) under either alternative.

If the sleep/wake data represents the sleep state, then a selection is made between two functions as to which is applicable based on the following decision rule: IFΔt≦k _(d)THENC _(t) =d(t)ELSEC _(t) =s _(m)(t)  Equation 15 The delay function d(t) is used only if the last data entry is the sleep state for a period of time is less than or equal to k_(d). Thus the same delay function d(t) as used in the preferred embodiment is also used in this alternative embodiment. The modified sleep function s_(m)(t) takes into account the delay function for a period of time equal to k_(d). Accounting for the delay function d(t) provides the following: s _(m)(t)=((C _(t−1)−(k _(w) *k _(d)))+(100−(100−C _(t−1))(1−1/k _(s))^(Δt−kd)))  Equation 16 where the first part of the equation represents the delay function d(t) and the second part represents the recovery of cognitive performance capacity C (f(t) portion of S7 c′).

A summation of the time components of the sleep/wake data is performed as each piece of sleep/wake data is handled with respect to the calculation of the cognitive performance capacity or prior to modulation of the final cognitive performance capacity with the time of day function m(t). The latter is shown in FIGS. 8( a)-(b). After the new cognitive performance capacity C_(t) is calculated, the method repeats to handle the next piece of sleep/wake data if the present piece is not the last piece. After the last piece the predicted cognitive performance E is calculated based on Equation 1 above and as detailed in the preferred embodiment.

Alternatively, the task function t(t) may be included at the same time of the time of day function m(t) instead of for each set of wake states by moving S9 b through S10 b to a position similar to that illustrated in FIG. 4( b).

It should be noted again that this method includes the processes and calculations based on Equations 1 through 12 expressed in their general form, with an alternative being the removal of the task function elements. Embodiments shall apply functions relating the variables involved according to empirical knowledge, resulting in specific expressions of those equations, as illustrated in the text and FIGS. 1-4( b) above (but not confined to these), which may be changed or refined according to the state of empirical knowledge.

APPLICATIONS OF THE INVENTION

There are a variety of potential applications of this invention. In its simplest application, the method according to the invention may be used to predict the impact of various idealized (i.e., unfragmented) amounts of nightly sleep on predicted cognitive performance E. Another practical application uses the method to predict the cognitive performance in an individual with fragmented sleep, either due to a sleep disorder such as sleep apnea or due to environmental disturbances such as airplane or train noises. Another practical application uses the method to predict the cognitive performance E of an individual changing his/her schedule for night shift work.

In another application, the method is used to retrospectively predict cognitive performance E in a commercial motor vehicle operator involved in a driving collision/traffic accident. In this application, the method is used first to predict an individual's level of cognitive performance E across some interval based on that individual's current work and sleep/wake schedule.

Another similar application is using the method to re-schedule sleep and wakefulness in order to optimize predicted cognitive performance E over an interval for a commercial motor vehicle operator. In this example, first we model a driver's predicted cognitive performance E based on his current sleep/wake schedule. The driver's current sleep/wake schedule is generated around the maximum duty hours allowed under the Federal Highway Administration's (FHWA) hours-of-service regulations. These regulations allow the driver to obtain a maximum 15 hours on-duty (maximum 10 hours driving plus five hours on-duty but not driving) followed by a minimum eight hours off-duty. The driver may continue this on/off-duty cycling until 60 hours on-duty has been accumulated—at which point the driver must take time off until seven days has elapsed since he commenced duty. An alternative work schedule also allowed under current FHWA regulations is based on a schedule of 12 hours on-duty and 12 hours off-duty with the underlying assumption that the driver sleeps eight of his 12 hours off-duty. The use of this invention will allow the selection of the schedule that allows for maximizing the driver's cognitive performance levels throughout a period of time.

Although described above in connection with a variety of specific activities, this invention has many other applications. The method for predicting cognitive performance will provide critical information for managing both individual and group productivity. For example, in military operational planning, this method will enable commanders to determine precisely, based on prior sleep history and duties performed, each soldier's current and predicted level of cognitive performance. Commanders can also input a likely sleep/wake and work schedules and thereby predict a soldier's cognitive performance throughout an impending mission. Throughout conduct of the mission itself, the latter cognitive performance predictions (originally based on likely sleep/wake and work schedules) can be updated based on actual sleep acquired and work performed by an individual soldier. The ability to project future cognitive performance will allow commanders to optimize troop performance during continuous operations by, for example, planning sleep/wake and duty schedules around the mission to optimize cognitive performance, selecting those troops or combinations of troops whose predicted cognitive performance will be maximal at a critical time, etc. This method will assist in maximizing productivity at both the individual level and unit level.

This invention may be employed in a variety of commercial applications covering many occupational areas for purposes of optimizing output (productivity). The invention provides managers with the capability to plan operations and regulate work hours to a standard based on objective cognitive performance predictions. This is in contrast to the frequently used method of regulating work hours by time off-duty (a relatively poor predictor of sleep/wake patterns and performance of tasks, and consequently a poor predictor of cognitive performance) or by generating alertness/sleepiness predictions (which, as noted above, do not always correspond to cognitive performance). The invention can be “exercised” in hypothetical sleep/wake and duty scenarios to provide an estimate of cognitive performance under such scenarios. To the extent that optimizing cognitive performance is of interest to the general public, there is a possibility for use in a variety of applications.

This invention also may be used in conjunction with drugs to alter the sleep/wake cycle of an individual and/or optimize or minimize the cognitive performance level of an individual as needed and/or desired.

This invention also can work conjunctionally with the concepts of particle swarm theory/algorithms. Particle swarm algorithms are routinely used to optimize the throughput of containers through a ship port or to optimize the use of workers within a work group to perform tasks over a given period. An example of an application is the planning of a mission for an army unit by its commander.

The method may also be used to gauge and evaluate the cognitive performance effects of any biomedical, psychological, or other (e.g., sleep hygiene, light therapy, etc.) treatments or interventions shown to improve sleep. Examples of these include but are not limited to patients with overt sleep disorders, circadian rhythm disorders, other medical conditions impacting sleep quality and/or duration, poor sleep hygiene, jet lag, or any other sleep/wake problem. Currently, the efficacy of treatments for improving sleep is determined by comparing baseline polysomnographic measures of nighttime sleep and some measure of daytime alertness (e.g., the MSLT, the Maintenance of Wakefulness Test (MWT), the Stanford Sleepiness Scale or the Karolinska Sleepiness Scale) with the same measures obtained after treatment. Both treatment efficacy and the likely impact on performance during waking periods are inferred from the results on the daytime alertness tests. For example, the Federal Aviation Administration currently requires any commercial pilots diagnosed with sleep apnea to undergo treatment. Such treatment is followed by daytime alertness testing on a modified version of the MWT. During the MWT, pilots are put in a comfortable chair in a darkened room and instructed to try to remain awake for extended periods. If the pilots are able to avoid overt sleep under these sleep-conducive conditions then they are deemed fit for duty. The inference is that the minimal ability to maintain wakefulness at a discrete point in time translates into the ability to operate an aircraft safely (i.e., it is inferred that alertness is equivalent to cognitive performance). However, sleep deprivation can affect cognitive performance even when it does not result in overt sleep, particularly during an alertness test when for various reasons the individual may be highly motivated to stay awake.

In contrast, the current method allows cognitive performance to be estimated directly from measured sleep parameters considered in conjunction with the time of day and performance of tasks. The advantages of this method over current methods for evaluating treatment efficacy are: (1) the motivations and motivation levels of the patients being tested cannot affect results (cognitive performance determinations); and (2) the method allows numerical specification and prediction of cognitive performance across all projected waking hours rather than indicating alertness at a discrete, specified point in time. Thus, the method provides a continuous scale for gauging cognitive performance across time rather than providing only a minimal “fitness for duty” determination based on the patient's ability to maintain EEG-defined wakefulness at a specific time.

The method may also be used clinically as an adjunct for diagnosing sleep disorders such as narcolepsy and idiopathic CNS hypersomnolence. Equally important, it may also be used to differentiate among sleep disorders. The latter is critical to the course of treatment, and consequent treatment efficacy depends on a valid and reliable diagnosis. For example, sleep apnea and periodic limb movements during sleep are characterized by nighttime sleep disruption (i.e., partial sleep deprivation) accompanied by daytime cognitive performance deficits. In contrast, narcolepsy and idiopathic hypersomnolence tend to be characterized by apparently normal nighttime sleep, but accompanied by daytime cognitive performance deficits. Based on the apparently normal nighttime sleep in the latter two groups, the invention would predict relatively normal cognitive performance. Thus, a discrepancy between predicted cognitive performance (based on the current invention) and observed or measured cognitive performance could be used to distinguish one sleep disorder from another. For example, narcolepsy, idiopathic hypersomnolence, or other CNS-related causes of daytime cognitive performance deficits (where no sleep deficit is apparent) could be distinguished from sleep apnea, periodic limb movements, or other causes of daytime cognitive deficits (where impaired sleep is evident).

Although the present invention has been described in terms of particular preferred embodiments, it is not limited to those embodiments. Alternative embodiments, examples, and modifications which would still be encompassed by the invention may be made by those skilled in the art, particularly in light of the foregoing teachings.

Furthermore, those skilled in the art will appreciate that various adaptations and modifications of the above-described preferred embodiments can be configured without departing from the scope and spirit of the invention. Therefore, it is to be understood that, within the scope of the appended claims, the invention may be practiced other than as specifically described herein. 

1. A method comprising: modeling a circadian rhythm having a first sinusoidal curve with a 24 hour period and a second sinusoidal curve with a 12 hour period using a processor, calculating a cognitive level of a person using a processor based on the person's sleep/wake data received from an actigraph or a polysomnography system, calculating a predicted cognitive performance based on said circadian rhythm and said cognitive level using a processor.
 2. The method according to claim 1, wherein said sleep/wake data contains a series of epochs where each epoch is classified as sleep or wake.
 3. A system comprising: at least one input device for receiving sleep/wake data, a microprocessor including means for modeling a circadian rhythm, means for determining a cognitive level of a person based on the person's sleep/wake data at predetermined fixed intervals having an identical time period, wherein there are multiple intervals in each 24 hour period and means for calculating a predicted cognitive performance in response to said determination of said cognitive level based on said circadian rhythm and said cognitive level, and a display to show the predicted cognitive performance.
 4. The system according to claim 3, wherein said input device is in communication with an actigraph.
 5. The system according to claim 3, wherein said input device is in communication with a polysomnography system.
 6. A computer program product stored on a computer readable medium for predicting a cognitive performance level of an individual, the computer program product comprising: first program instructions to model a circadian rhythm, second program instructions to determine a cognitive level of a person based on the person's sleep/wake data that includes a series of epochs where each equal length epoch is classified as sleep or wake, and third program Instructions to calculate a predicted cognitive performance for each epoch of data based on said circadian rhythm and said cognitive level.
 7. The computer program product according to claim 6, wherein the circadian rhythm has a first sinusoidal curve with a 24 hour period and a second sinusoidal curve with a 12 hour period.
 8. A method for providing a cognitive performance level comprising: receiving a data series representing at least one wake state and at least one sleep state an actigraph or a polysomnography system, selecting a function based on the data series, wherein the function is selected from a group consisting of a wake function, a sleep function, and a sleep inertia function, where the wake function is expressed as follows w(t)=C _(t−1) −k _(w) where k_(w) is a positive function, the sleep function is expressed as follows s(t)=C _(t−1)+(100−C _(t−1))/k _(s) where k_(s) is a time constant, and the sleep inertia function is expressed as follows i(t)=C _(SW)*[0.75+0.025(t−t _(LS))−(0.025(t−t _(LS)))²] where t_(LS) is time when the last sleep state occurred and C_(SW) is the cognitive level at the last sleep state, determining a cognitive performance capacity using the selected function using a processor, modulating the cognitive performance capacity with a time of day value using a processor, and providing the modulated value.
 9. A system comprising: at least one input device for receiving sleep/wake data, wherein said sleep/wake data contains a series of epochs where each equal length epoch is classified as sleep or wake, a microprocessor including means for modeling a circadian rhythm, means for determining a cognitive level of a person based on the person's sleep/wake data, and means for calculating a predicted cognitive performance based on said circadian rhythm and said cognitive level, and a display to show the predicted cognitive performance.
 10. A system comprising: at least one input device for receiving sleep/wake data, a microprocessor including means for modeling a circadian rhythm, wherein the circadian rhythm has a first sinusoidal curve with a 24 hour period and a second sinusoidal curve with a 12 hour period, means for determining a cognitive level of a person based on the person's sleep/wake data, and means for calculating a predicted cognitive performance based on said circadian rhythm and said cognitive level, and a display to show the predicted cognitive performance.
 11. A system comprising: at least one input device for receiving sleep/wake data, wherein the person's sleep/wake data includes a series of epochs where each equal length epoch is classified as sleep or wake, a microprocessor including means for modeling a circadian rhythm, means for determining a cognitive level of a person based on the person's sleep/wake data, and means for calculating a predicted cognitive performance based on said circadian rhythm and said cognitive level, said means for calculating the predicted cognitive performance performs the calculation for each epoch of data.
 12. The system according to claim 11, further comprising a display to show the predicted cognitive performance.
 13. A method comprising: calculating a cognitive level of a person using a processor based on the person's sleep/wake data received from an actigraph or a polysomnography system, retrieving from memory a time of day value for a time of day that the calculated cognitive level is being calculated for, where the time of day value is from a data series of time of days values that together approximate a curve having a first curve component having a 24 hour period and a second curve component having a 12 hour period, calculating a predicted cognitive performance using a processor based on said retrieved time of day value and said cognitive level, and displaying said calculated predicted cognitive performance. 